- Title
- Efficient graph learning for anomaly detection systems
- Creator
- Febrinanto, Falih
- Date
- 2023
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/194703
- Identifier
- vital:18386
- Identifier
-
https://doi.org/10.1145/3539597.3572990
- Identifier
- ISBN:9781450394079 (ISBN)
- Abstract
- Anomaly detection plays a significant role in preventing from detrimental effects of abnormalities. It brings many benefits in real-world sectors ranging from transportation, finance to cybersecurity. In reality, millions of data do not stand independently, but they might be connected to each other and form graph or network data. A more advanced technique, named graph anomaly detection, is required to model that data type. The current works of graph anomaly detection have achieved state-of-the-art performance compared to regular anomaly detection. However, most models ignore the efficiency aspect, leading to several problems like technical bottlenecks. This project mainly focuses on improving the efficiency aspect of graph anomaly detection while maintaining its performance. © 2023 Owner/Author.
- Publisher
- Association for Computing Machinery, Inc
- Relation
- 16th ACM International Conference on Web Search and Data Mining, WSDM 2023, Singapore, 27 February to 3 March 2023, WSDM 2023 - Proceedings of the 16th ACM International Conference on Web Search and Data Mining p. 1222-1223
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- © 2023 Copyright held by the owner/author(s).
- Subject
- Anomaly detection; Efficient model; Graph learning
- Reviewed
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